Apparatus for partitioning compressed satellite image and the method thereof

ABSTRACT

Disclosed an apparatus and method of partitioning compressed satellite image, and more specifically, the present invention relates to a technique for forming index information on the compressed satellite image using the starting point and the length of a compressed section so as to randomly access each compressed section in the wavelet-based compressed satellite image recommended through CCSDS. The present invention minimizes costs for long-term storage of the satellite image data by immediately indexing, partitioning, and storing the compressed satellite data in a storage without recovering the compressed satellite data, rapidly provides high-quality satellite images for users by minimizing information loss while recovering the compressed image, and thereby being effective for being able to reduce computing resources needed to recover the compressed image data.

TECHNICAL FIELD

Disclosed an apparatus and method of partitioning compressed satelliteimage, and more specifically, the present invention relates to atechnique for forming index information on the compressed satelliteimage using the starting point and the length of a compressed section soas to randomly access each compressed section in the wavelet-basedcompressed satellite image recommended through CCSDS (consultativecommittee for space data system).

BACKGROUND

As observation technologies through satellite evolve, high-precision (ordefinition), high-resolution satellite images have been able to beacquired. The utilization of these satellite images is increasing inmany areas, such as monitoring and analysis of crops, land management,cartography, defense, environmental management, etc. However, very largeamount of data have to be transferred and processed for high-precision(or definition), high-resolution satellite images.

Therefore, highly efficient data compression techniques are required fordelivering large amounts of satellite image data from satellite to theground by using limited frequency resources, and so far mostly DCT-basedJPEG-like compression method has been adopted for this satellite imagedata compression. However, DCT-based compression method occurs blockingnoise, which causes the distortion factor to be generated and then thequality of the satellite images to be degraded, and thus additionalprocessing for compensating the distortion was required.

In order to complement the disadvantages of DCT-based JPEG-like methodsand to get a higher image quality with the same compression ratio,wavelet-based compression technique used in JPEG2000 was applied, andCCSDS, which is an international consultative body with respect to datacommunications in the field of space for the compression of satelliteimages, recommends wavelet-based image compression methods optimized forthe images observed in satellites.

FIG. 1 shows the figure of LEO (Low Earth Orbit) satellite observationsby the prior arts. LEO satellites are currently used for the acquisitionof high resolution satellite images, and ground observations areperformed by photographing the surface of ground area within observableviewing angle and swath in the middle of moving the satellite along itsorbit and transmitting images to the ground.

FIG. 2 shows the shape of step-by-step process of satellite observationsimages by conventional prior arts, and the shape becomes the form ofstrip. The number of column of image in the shape of strip is fixedvalue in accordance with the resolution and swath of the satelliteimage, the number of line is variable over time, and the value is verylarge from hundreds of thousands to millions of lines. Satellite imagedata transferred to the ground is stored as image product afterprocessing the image data at each level, or provided to users. Thenumber of each level and the operations performed in each level can bedifferently designated depending on satellite, but the level can bedivided into level, 0 (1), 1A (2), 1R (3), 1G (4) in general. Each levelis defined as follows.

-   -   Level 0 (1): Images made in the form of strip by channel        decoding, decrypting and decompressing the data received from        the satellite.    -   Level 1A (2): Performing tasks for combining images in each        observation wavelength by using level 0 images or some        compensations tasks. The shape (form) of image is in the form of        strip like the image in level 0.    -   Level 1R (3): Performing tasks compensating radiometric        characteristics by using level 1A images. The shape (form) of        image is in the form of scene.    -   Level 1G (4): Performing tasks compensating geometric        characteristics by using level 1R images, and outputting images        being mapped into a map.

In the most of the conventional satellite image processing method,compressed images are transmitted from satellite as shown in FIG. 2 inthe form of strip and then decompressed. And images ordered by a userare produced by processing the radiation correction and geometriccorrection. The uncompressed images are stored in a long-term storage inthe state that various calibrations are processed in order to produceadditional images. The shape to be stored in a long-term storage islevel 0 (1) or level 1A (2). Level 1R (3) and level 1G (4) are the formin which images are provided to a buyer ordering the images.

This kind of processing method has the following disadvantages.

First, images are stored in long-term storage in uncompressed state, andthus the construction costs and TCO (Total Cost Ownership) forconstructing storage system for storing data are severely required.

Secondly, images are stored by applying algorithms, radiationcalibration, geometric correction, etc., to compensate for variousdistortions of satellite prior to storing images in long-term storage,and thus it is impossible to improve the quality of images for theprevious images prior to the application time point of the improvedcompensation algorithms because original images cannot be recovered evenimproved compensation algorithms are developed.

Thirdly, it takes a long waiting time for a user to acquire usableimages, because of uncompressing the strip images transmitted fromsatellite and performing calibration process for the images.

Fourthly, in the case of continuously storing compressed satellite imagedata in the wavelet-based compression method recommended from CCSDS, ifan error occurs in the data due to noise during transmitting the datafrom satellite, it is impossible to process the remaining data from thepart where the error occurs because there is no identifier that canidentify the compressed segment in the compressed satellite image data.

FIG. 3 shows the structure of a conventional compressed satellite imagefile, where it comprises heather (10) and data (11). There is a fixedpattern identifier which can identify the beginning of each compressedsegment in the header, and thus the image data in any compressedinterval can be independently decompressed. However, there is a problemthat the entire frame should be decompressed in a sequence in order todecompress each compressed section because there is no fixed patternidentifier (marker) being able to identify each segment in the header ofwavelet-based compression method recommended in CCSDS.

A method of approaching any line of a strip type image is disclosed inKorean Patent No. 0945733, in which the method allows random access, notsequential, to an images section requiring decompression by using indexfile which is made for the information for each block interval of acompressed image. Wherein, index information consisting of startingposition and the length for the interval of image line block data unitis generated. There is a fixed pattern identifier identifying the startpoint in the image line block data, and thus index information can begenerated by identifying this identifier. Moreover, the index for randomaccess is generated with physical location information of each imageline data in the stored file for the line data of line-by-linecompressed image. This method is available in the existing DCT-basedcompression method, but wavelet-based compression scheme does not havedistinguishable identifier for a compressed segment, and it is thereason why the relationship between each compressed interval is notindependent each other due to the characteristics of wavelet transform.

SUMMARY

The present invention is invented in order to resolve the problems ofthe conventional prior arts described above, and the objectives of thepresent invention are to identify each compressed image interval byusing source packet being defined in CCSDS recommendation PacketTelemetry (CCSDS 102.0-B-5) in order to enable random access for thecompressed data by the compress method based on wavelet transformrecommended by CCSDS, minimize the costs for long-term storage of thesatellite image data by storing the compressed data withoutdecompression and the index information comprising starting point andthe length of the interval in a storage, provide satellite image havingbetter quality to a user by preventing the information loss occurred inthe course of correcting the images after decompressing the compressedimages, and provide satellite images to a user faster as well as savethe computing resources required for long-term storing of compressedimages data.

In order to achieve the above objectives, the apparatus and method forsegmenting satellite compressed image in accordance with the presentinvention is characterized by comprising; receiving source packetincluding satellite compressed image segments continuously transmittedfrom satellites; extracting satellite compressed image segments and thelength information of said satellite compressed image segments from saidreceived source packet; generating index of said satellite compressedimage segments by using said length information and the subsequentstarting location; and storing said satellite compressed image segmentsand said generated index.

Wherein, said source packet comprises source data comprising auxiliarydata field comprising variable sized satellite compressed imagesegments, and satellite attitude and position information; and packetdata comprising header data including property information of saidsource data; and the length information of said packet data.

Said compressed satellite image segments are generated through wavelettransform and BPE (bit plane encoding),

Next said continuous starting position is calculated by adding thelength of the previous satellite compressed image segments from thestarting position of the previous satellite compressed image segments,

Said stored specific compressed satellite image segments areindependently decompressed to original image by additionally referringto previous 3 segments and next 3 segments.

The apparatus and method for partitioning compressed satellite image inaccordance with the present invention have effective to directly storethe compressed satellite image data with index in a storage withoutdecompression, to minimize the costs for long-term storing of thecompressed satellite image data, to provide a better quality ofsatellite image to a user by preventing information loss occurred in thecourse of correcting said images after decompressing the compressedimages, and provide satellite images to a user faster as well as savethe computing resources required for long-term storing of compressedimages data.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention, illustrate the preferred embodiments ofthe invention, and together with the description, serve to explain theprinciples of the present invention. In drawings:

FIG. 1 shows low earth orbit satellite observations in accordance withconventional prior arts.

FIG. 2 shows step-by-step processing flows and image shape of satelliteobservation image in accordance with conventional prior arts.

FIG. 3 shows the structure of a compressed satellite image file inaccordance with conventional prior arts.

FIG. 4 shows the flows of step-by-step processes for compression andtransmission of satellite images in accordance with a preferredembodiment of the present invention.

FIG. 5 shows a conceptual diagram of wavelet transform for the stripshaped satellite image data in accordance with a preferred embodiment ofthe present invention.

FIG. 6 shows a block diagram of relocating geometrically correlatedcoefficients in the wavelet transformed image frame in accordance with apreferred embodiment of the present invention.

FIG. 7 shows a diagram illustrating the locations in wavelet transformimage frame corresponding to each segment of wavelet transformed imagein accordance with a preferred embodiment of the present invention.

FIG. 8 shows a structure of source packet for indexing segments ofcompressed satellite image data in accordance with a preferredembodiment of the present invention.

FIG. 9 shows a fundamental structure of a transport frame in accordancewith a preferred embodiment of the present invention.

FIG. 10 shows the structure of consecutive CADU in accordance with apreferred embodiment of the present invention.

FIG. 11 shows step-by-step process receiving flows of satellite imagedata and a flowchart of indexing processes of compressed satellite imagesegments in accordance with a preferred embodiment of the presentinvention.

DETAILED DESCRIPTION

Hereinafter, the apparatus and method of partitioning compressedsatellite image in accordance with preferred embodiments of the presentinvention are described in detail with reference to the accompanyingdrawings.

FIG. 4 shows the flows of step-by-step processes for compression andtransmission of satellite images in accordance with a preferredembodiment of the present invention, especially the flowchart ofcompressing and transmitting the strip shaped satellite image data withwavelet transform and BPE according to CCSDS. Said strip shapedsatellite image data is divided into a plurality of image frames, andeach image frame is wavelet transformed with the unit of a frame. Aplurality of segments comprise said wavelet transformed coefficients,each segment is encoded with a bit plane encoding (CCSDS BPE codedsegment), a source data comprises at least more than one said segments,and a source packet is created by adding a header to said source data.Said source packet is encrypted, encrypted source packet is separatedand VCDU (Virtual Channel Data Unit) is created, transport frame datafield is generated by performing randomization after Reed-SolomonEncoding that of attaching VCDU header to said VCDU, and then transportframe can be created by attaching transport frame header to saidtransport frame data field. CADU (Channel Access Data Unit) is createdby adding frame synchronization identifier (marker) to said transportframe and CADU becomes to be transmitted through a channel.

So far, the flow for compressing the source image frame and thentransmitting the compressed source image is described and hereinaftersaid flow will be described in more detail. First of all, the processesfor compressing the source image frame and creating source packet areexplained.

FIG. 5 shows a conceptual diagram of wavelet transform for the stripshaped satellite image data recommended in CCSDS. First of all, thestrip shaped satellite image data is divided into image framescomprising several lines, wavelet transform is performed for the entireimage frame, coefficients are relocated with blocks, blocks are groupedby the unit of segments, each block is zigzag scanned and entropy codedby the unit of bit plane for unit of each segment, and thereby originalimage cannot be recovered by processing just a single block alone havingcorresponding image lines like a DCT because there exists relationshipamong blocks.

For example, if we assume that a 32×32 image frame exists beforeperforming wavelet transform, for the case of performing 3-level wavelettransform for this image frame, firstly a single 16×16 DC block andthree high frequency blocks are created (level 1), secondly said single16×16 DC block is divided into a single 8×8 DC block and 3 highfrequency blocks (level 2), and finally said 8×8 DC block is dividedinto a single 4×4 DC block and three high frequency blocks (level 3).

From said wavelet transformed image frame, a block comprises 64 subbandcoefficients, DC, P0, P1, P2, C0, C1, C2, G0, G1, G2, corresponding to aparticular area of original image frame, said block comprises as shownin FIG. 6. Therefore, 32×32 sized wavelet transformed image framecreates 16 (4×4) said blocks (8×8), a single segment is created bycollecting 4 DC said blocks (8×8) together located at each line of DCblocks, and BPE (Bit Plane Encoding) recommended in CCSDS is to performencoding by the unit of segment for increasing compression ratioaccording to correlation among a plurality of said blocks. Thecompressed image frames as above, are then transmitted to the groundfrom the first created compressed stream one by one as shown in FIG. 7.FIG. 7 shows a diagram illustrating where the transmitted segments arelocated in wavelet transformed image frame.

In the compress method recommended in CCSDS, wavelet transform isperformed with 3-level, and thus the transform is not independentlyprocessed as a whole, but a single DC coefficient corresponds to DCinformation for 8×8 pixels of original image. Moreover, a single blockincludes 8×8 pixels of a DC and ACs (high frequency) components.

So far, in the flowchart of compressing strip shaped satellite imagedata with wavelet transform according to CCSDS and transmitting thewavelet transformed image data as shown in FIG. 4, the steps until BPE(Bit Plane Encoding) have been explained. FIG. 8 shows a structure forcreating source packet including said BPE coded segments. As shown inFIG. 8, source packet comprises header and packet data field, and saidpacket data field auxiliary header including aux data length and filldata length information and source data, and said source data comprisesCCSDS BPE coded segments, aux data and fill data. In FIG. 8, the reallength of CCSDS BPE coded segments can be extracted from packet datalength, the header of packet data field and the length of aux datafield. In the receiving side, compression section, the start positionand the size of the region in line can be extracted from the sizeinformation of said segment and line number.

Source packet created as above is encrypted, the encrypted source packetis divided into appropriate packet to be able to be transmitted, VCDU iscreated, and then transport frame data field comprises said VCDU, towhich Reed-Solomon check symbol is added through channel coding. Inaddition, transport frame is constructed by adding header to saidtransport frame data field, CASU is created by adding synchronizationidentifier to the randomized transport frame, and finally successiveCASU is transmitted via channel. The structure of such transport frameand consecutive CADU are shown in FIG. 9 and FIG. 10, respectively.

So far, the transmission of source packet has been explained, and FIG.11 shows the flows to receive source packet by using a receiving devicemeeting CCSDS recommendation (Packet Telemetry: CCSDS 102.0-B-5) and tomake index for compression.

1) First, frame synchronization identifier is extracted from receivedCADU, and frame synchronization is performed, and CADU is extracted.CADU is a data unit used for transmitting data on channel, which has theform attaching synchronization identifier to transport frame in CCSDS(Referring to FIG. 10). Transport frame is extracted from CADU byidentifying attached sync. marker (1ACFFC1Dh) with fixed 32-bit pattern.

2) Next, randomized transport frame is de-randomized in order to providenoise robust characteristics for said extracted transport frame, errorcorrection is performed by using Reed-Solomon check symbol, which isattached for the case of being damaged by noise in the course oftransmission, said Reed-Solomon check symbol is removed from transportframe, and VCDU is extracted.

3) Source packet separated for transmission is re-assembled by usingheader of extracted VCDU, encrypted source packet is decrypted and thensource packet is extracted.

4) Said decrypted source packet has the structure shown in FIG. 8, andthe length information of BPE coded segment is calculated by extractingthe length information of packet data included in source packet and thelength information of aux data field.

5) The starting position of BPE coded segments stored in receivingdevice in succession can be index information, the sequence number ofimage frame and the segments belonged in each image frame can be indexinformation, and the next starting position can be calculated by addingthe length information of segments to said starting information.Therefore, the starting position and the length information of eachsegment in the consecutive compressed stream stored as a file aremanaged by being stored in the index file in the order of image frameand the sequence number belonged in each image frame.

For a preferred embodiment of the present invention, correspondingsegments should be able to be independently recovered from correspondingstarting position by referring to index file for any of the BPE-codedsegment. However, it is not independent of each other between blocksbecause the block contains some of the ingredients of adjacent blocksdue to the nature of the wavelet transform. Therefore, there needs tolook at how much influencing among blocks in the case of performingreverse transform for recovering original image.

In another word, the indexing of compressed image received fromsatellite will be reasonably performed by figuring out the range ofbeing able to independently perform wavelet transform for the specificsegments comprising a plurality of blocks. To do this, the inversewavelet transform formula needs to be inspected.

Two sets of synthesis filter coefficients, that is, low-pass filtercoefficients (qi) and high-pass filter coefficients (pi) are used for9/7 floating point inverse wavelet transform, and the coefficients aresummarized in Table 1. Said synthesis filter coefficients are used forsynthesis filtering computation, said the synthesis filteringcomputation is called as inverse wavelet transform and expressed asshown in Table 1.

TABLE 1 Synthesis filter coefficients i Low-pass filter, q_(i) High passfilter, p_(i) 0 0.788485616406 −0.852698679009 1 0.4180922732220.377402855613 2 −0.040689417609 0.110624404418 3 −0.064538882629−0.023849465020 4 −0.037828455507

Wherein, [Equation 1] of the signal wavelet coefficients, Cj, and Djshould be extended to subscript, j, j==−m−1 and j=N−1+m=N−1−m,respectively at boundary area (m<0 or m>0).

$\begin{matrix}{\left. \begin{matrix}{x_{2\; j} = {{\sum\limits_{n = {- 1}}^{1}\; {q_{2\; n}C_{j + n}}} + {\sum\limits_{n = {- 2}}^{1}\; {p_{{2\; n} + 1}D_{j + n}}}}} \\{x_{{2\; j} + 1} = {{\sum\limits_{n = {- 1}}^{2}\; {q_{{2\; n} - 1}C_{j + n}}} + {\sum\limits_{n = {- 2}}^{2}\; {p_{2\; n}D_{j + n}}}}}\end{matrix} \right\} {\quad \left( {{j = 0},1,\ldots \mspace{14mu},{N - 1}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

Next, integer inverse wavelet transform is expressed as [Equation 2],and the processes for mapping two wavelet coefficients, Cj (low-passfilter set) and Dj (high-pass set) to original image signal vector, xj,are listed as the same processes as those of 9/7 floating point inversewavelet transform.

$\begin{matrix}{\mspace{79mu} {{x_{0} = {C_{0} + \left\lfloor {{- \frac{D_{0}}{2}} + \frac{1}{2}} \right\rfloor}}\mspace{79mu} {x_{2\; j} = {C_{f} + \left\lfloor {{- \frac{D_{j - 1} + D_{j}}{4}} + \frac{1}{2}} \right\rfloor}}\; \mspace{79mu} {{{{for}\mspace{14mu} j} = 1},\ldots \mspace{14mu},{N - 1}}\mspace{79mu} {x_{1} = {D_{0} + \left\lfloor {{\frac{9}{16}\left( {x_{0} + x_{2}} \right)} - {\frac{1}{16}\left( {x_{2} + x_{4}} \right)} + \frac{1}{2}} \right\rfloor}}{x_{{2\; j} + 1} = {D_{j} + \left\lfloor {{\frac{9}{16}\left( {x_{2\; j} + x_{{2\; j} + 2}} \right)} - {\frac{1}{16}\left( {x_{{2\; j} - 2} + x_{{2\; j} + 4}} \right)} + \frac{1}{2}} \right\rfloor}}\mspace{79mu} {{{{for}\mspace{14mu} j} = 1},\ldots \mspace{14mu},{N - 3}}{x_{{2\; N} - 3} = {D_{N - 2} + \left\lfloor {{\frac{9}{16}\left( {x_{{2\; N} - 4} + x_{{2\; N} - 2}} \right)} - {\frac{1}{16}\left( {x_{{2\; N} - 6} + x_{{2\; N} - 2}} \right)} + \frac{1}{2}} \right\rfloor}}\mspace{79mu} {x_{{2\; N} - 1} = {D_{N - 1} + \left\lfloor {{\frac{9}{8}x_{{2\; N} - 2}} - {\frac{1}{8}x_{{2\; N} - 4}} + \frac{1}{2}} \right\rfloor}}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

Hereinafter, minimum section required for recovering one segment fromthe entire image frame will be explained from [Equation 1] and [Equation2].

In wavelet transform based CCSDS satellite image compression method,image frame size must be a multiple of 8 because the wavelet transformshould be performed across 3 levels, and each segment is composed ofcoefficients, which have geometrical relationship out of wavelettransformed coefficients.

By using these characteristics, image frame prior to performing wavelettransform can be divided into 8 lines as a transform section, and imagecan be decompressed by using minimum segments with the unit of thetransform section. Minimum segments required for decompression needprevious 3 segments and next 3 segments as the center segment containsthe image to be recovered.

Since partitioned processing using index as described in the presentinvention is accomplished with line-by-line of image, and row transformand column transform of wavelet transform are independent each other, soonly figuring out the influential range of column (direction) inversetransform is enough to derive minimum number of segment required fordecompressing image lines associated to a specific segment. Thus, theprevious level coefficients required for column direction inversetransform for each level are described in the followings. The notationsof the descriptions are explained as follows.

-   -   x_(ij) ^(ld), 1: level (1,2,3), d: direction (c, r), i: row        index, j: column index

The minimum range required for decompressing one segment of the entireimage frame for real number based inverse transform of [Equation 1] andinteger number based inverse transform of [Equation 2] is as follows.

1) Level 1 Inverse Transform

As described above, 8 lines of image frame can be associated with eachsegment. The number of image line associated with a specific segment canbe described as follows. s refers to the number of segment.

8s, 8s+1, 8s+2, 8s+3, 8s+4, 8s+5, 8s+6, 8s+7

The above notations can be modified as follows for separately assigningeven and odd components to inverse transform basic formula.

8s, 8s+1, 8s+2, 8s+2+1, 8s+4, 8s+4+1, 8s+6, 8s+6+1

Coefficients for level 1 column direction real number and integerinverse transform are summarized as shown [Table 2] and [Table 3].

TABLE 2 Transform Result low-pass coefficients high-pass coefficientsx_(8s,j) ^(1c) C_(4s−1,j) ^(1c), C_(4s,j) ^(1c), C_(4s+1,j) ^(1c)D_(4s−2,j) ^(1c), D_(4s−1,j) ^(1c), D_(4s,j) ^(1c), D_(4s+1,j) ^(1c)x_(8s+1,j) ^(1c) C_(4s−1,j) ^(1c), C_(4s,j) ^(1c), C_(4s+1,j) ^(1c),C_(4s+2,j) ^(1c) D_(4s−2,j) ^(1c), D_(4s−1,j) ^(1c), D_(4s,j) ^(1c),D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c) x_(8s+2,j) ^(1c) C_(4s,j) ^(1c),C_(4s+1,j) ^(1c), C_(4s+2,j) ^(1c) D_(4s−1,j) ^(1c), D_(4s,j) ^(1c),D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c) x_(8s+3,j) ^(1c) C_(4s,j) ^(1c),C_(4s+1,j) ^(1c), C_(4s+2,j) ^(1c), C_(4s+3,j) ^(1c) D_(4s−1,j) ^(1c),D_(4s,j) ^(1c), D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c), D_(4s+3,j) ^(1c)x_(8s+4,j) ^(1c) C_(4s+1,j) ^(1c), C_(4s+2,j) ^(1c), C_(4s+3,j) ^(1c)D_(4s,j) ^(1c), D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c), D_(4s+3,j) ^(1c)x_(8s+5,j) ^(1c) C_(4s+1,j) ^(1c), C_(4s+2,j) ^(1c), C_(4s+3,j) ^(1c),C_(4s+4,j) ^(1c) D_(4s,j) ^(1c), D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c),D_(4s+3,j) ^(1c), D_(4s+4,j) ^(1c) x_(8s+6,j) ^(1c) C_(4s+2,j) ^(1c),C_(4s+3,j) ^(1c), C_(4s+4,j) ^(1c) D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c),D_(4s+3,j) ^(1c), D_(4s+4,j) ^(1c) x_(8s+7,j) ^(1c) C_(4s+2,j) ^(1c),C_(4s+3,j) ^(1c), C_(4s+4,j) ^(1c), C_(4s+5,j) ^(1c) D_(4s+1,j) ^(1c),D_(4s+2,j) ^(1c), D_(4s+3,j) ^(1c), D_(4s+4,j) ^(1c), D_(4s+5,j) ^(1c)

TABLE 3 Transform Result low-pass coefficients high-pass coefficientsx_(8s,j) ^(1c) C_(4s,j) ^(1c) D_(4s−1,j) ^(1c), D_(4s,j) ^(1c)x_(8s+1,j) ^(1c) C_(4s−1,j) ^(1c), C_(4s,j) ^(1c), C_(4s+1,j) ^(1c),C_(4s+2,j) ^(1c) D_(4s−2,j) ^(1c), D_(4s−1,j) ^(1c), D_(4s,j) ^(1c),D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c) x_(8s+2,j) ^(1c) C_(4s+1,j) ^(1c)D_(4s,j) ^(1c), D_(4s,+1,j) ^(1c) x_(8s+3,j) ^(1c) C_(4s,j) ^(1c),C_(4s+1,j) ^(1c), C_(4s+2,j) ^(1c), C_(4s+3,j) ^(1c) D_(4s−1,j) ^(1c),D_(4s,j) ^(1c), D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c), D_(4s+3,j) ^(1c)x_(8s+4,j) ^(1c) C_(4s+2,j) ^(1c) D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c)x_(8s+5,j) ^(1c) C_(4s+1,j) ^(1c), C_(4s+2,j) ^(1c), C_(4s+3,j) ^(1c),C_(4s+4,j) ^(1c) D_(4s,j) ^(1c), D_(4s+1,j) ^(1c), D_(4s+2,j) ^(1c),D_(4s+3,j) ^(1c), D_(4s+4,j) ^(1c) x_(8s+6,j) ^(1c) C_(4s+3,j) ^(1c)D_(4s+2,j) ^(1c), D_(4s+3,j) ^(1c) x_(8s+7,j) ^(1c) C_(4s+2,j) ^(1c),C_(4s+3,j) ^(1c), C_(4s+4,j) ^(1c), C_(4s+5,j) ^(1c) D_(4s+1,j) ^(1c),D_(4s+2,j) ^(1c), D_(4s+3,j) ^(1c), D_(4s+4,j) ^(1c), D_(4s+5,j) ^(1c)

As shown in [Table 2] and [Table 3], maximum previous 2 and next 3coefficients are required for 4s stage coefficients as a center. Sincethe number of coefficients corresponding to one segment in level 1transform is 4, minimum number of segment required for beingdecompressed in this stage is previous 1 and next 1 segments. In otherwords, if s is 1, 2, 3, 4, 5, 6, 7, 8, 9th coefficients are needed,4˜7th coefficients belong to current segment. Both one segment for theprevious 2˜3th coefficients and one segment for the 8˜9th coefficientsare required for being compressed.

2) Level-2 Inverse Transform

Only low-pass components in level 1 transform are related in level 2inverse transform, and thus the following coefficients for derivinglow-pass coefficients are enough to be considered.

C _(4s−1,j) ^(3c) , C _(4s,j) ^(1c) , C _(4s+1,j) ^(1c) , C _(4s+2,j)^(1c) , C _(4s+3,j) ^(1c) , C _(4s+4,j) ^(1c) , C _(4s+5,j) ^(1c)

The results of level 2 inverse transform corresponding to the abovecoefficients are as follows.

x _(4s−1,j) ^(2c) , x _(4s,j) ^(2c) , x _(4s+3,j) ^(2c) , x _(4s+2,j)^(2c) , x _(4s+3,j) ^(2c) , x _(4s+4,j) ^(2c) , x _(4s+5,j) ^(2c)

Coefficients required for level 2 column direction real number andinteger number inverse transform are summarized as shown in [Table 4]and [Table 5].

TABLE 4 Transform Result low-pass coefficients high-pass coefficientsx_(4s−1,j) ^(2c) C_(2s−2,j) ^(2c), C_(2s−1,j) ^(2c), C_(2s,j) ^(2c),C_(2s+1,j) ^(2c) D_(2s−3,j) ^(2c), D_(2s−2,j) ^(2c), D_(2s−1,j) ^(2c),D_(2s,j) ^(2c), D_(2s+1,j) ^(2c) x_(4s,j) ^(2c) C_(2s−1,j) ^(2c),C_(2s,j) ^(2c), C_(2s+1,j) ^(2c), D_(2s−2,j) ^(2c), D_(2s−1,j) ^(2c),D_(2s,j) ^(2c), D_(2s+1,j) ^(2c) x_(4s+1,j) ^(2c) C_(2s−1,j) ^(2c),C_(2s,j) ^(2c), C_(2s+1,j) ^(2c), C_(2s+2,j) ^(2c) D_(2s−2,j) ^(2c),D_(2s−1,j) ^(2c), D_(2s,j) ^(2c), D_(2s+1,j) ^(2c), D_(2s+2,j) ^(2c)x_(4s+2,j) ^(2c) C_(2s,j) ^(2c), C_(2s+1,j) ^(2c), C_(2s+2,j) ^(2c)D_(2s−1,j) ^(2c), D_(2s,j) ^(2c), D_(2s+1,j) ^(2c), D_(2s+2,j) ^(2c)x_(4s+3,j) ^(2c) C_(2s,j) ^(2c), C_(2s+1,j) ^(2c), C_(2s+2,j) ^(2c),C_(2s+3,j) ^(2c) D_(2s−1,j) ^(2c), D_(2s,j) ^(2c), D_(2s+1,j) ^(2c),D_(2s+2,j) ^(2c), D_(2s+3,j) ^(2c) x_(4s+4,j) ^(2c) C_(2s+1,j) ^(2c),C_(2s+2,j) ^(2c), C_(2s+3,j) ^(2c) D_(2s,j) ^(2c), D_(2s+1,j) ^(2c),D_(2s+2,j) ^(2c), D_(2s+3,j) ^(2c) x_(4s+5,j) ^(2c) C_(2s+1,j) ^(2c),C_(2s+2,j) ^(2c), C_(2s+3,j) ^(2c), C_(2s+2,j) ^(2c) D_(2s,j) ^(2c),D_(2s+1,j) ^(2c), D_(2s+2,j) ^(2c), D_(2s+3,j) ^(2c), D_(2s+2,j) ^(2c)

TABLE 5 Transform Result low-pass coefficients high-pass coefficientsx_(4s−1,j) ^(2c) C_(2s−2,j) ^(2c), C_(2−1,j) ^(2c), C_(2s,j) ^(2c),C_(2s+1,j) ^(2c) D_(2s−3,j) ^(2c), D_(2s−2,j) ^(2c), D_(2s−1,j) ^(2c),D_(2s,j) ^(2c), D_(2s+1,j) ^(2c) x_(4s,j) ^(2c) C_(2s,j) ^(2c)D_(2s−2,j) ^(2c), D_(2s,j) ^(2c) x_(4s+1,j) ^(2c) C_(2s−1,j) ^(2c),C_(2s,j) ^(2c), C_(2s+1,j) ^(2c), C_(2s+2,j) ^(2c) D_(2s−2,j) ^(2c),D_(2s−1,j) ^(2c), D_(2s,j) ^(2c), D_(2s+1,j) ^(2c), D_(2s+2,j) ^(2c)x_(4s+2,j) ^(2c) C_(2s+1,j) ^(2c) D_(2s,j) ^(2c), D_(2s+1,j) ^(2c)x_(4s+3,j) ^(2c) C_(2s,j) ^(2c), C_(2s+1,j) ^(2c), C_(2s+2,j) ^(2c),C_(2s+3,j) ^(2c) D_(2s−1,j) ^(2c), D_(2s,j) ^(2c), D_(2s+1,j) ^(2c),D_(2s+2,j) ^(2c), D_(2s+3,j) ^(2c) x_(4s+4,j) ^(2c) C_(2s+2,j) ^(2c)D_(2s+1,j) ^(2c), D_(2s+2,j) ^(2c) x_(4s+5,j) ^(2c) C_(2s+1,j) ^(2c),C_(2s+2,j) ^(2c), C_(2s+3,j) ^(2c), C_(2s+4,j) ^(2c) D_(2s,j) ^(2c),D_(2s+1,j) ^(2c), D_(2s+2,j) ^(2c), D_(2s+3,j) ^(2c), D_(2s+4,j) ^(2c)

As shown in [Table 4] and [Table 5], maximum previous 3 and next 4coefficients are required for 2sth coefficients as a center. Since thenumber of coefficients corresponding to one segment in level 2 transformis 2, minimum number of segment required for being decompressed in thisstage is previous 2 and next 2 segments. In other words, if s is 2, 1,2, 3, 4, 5, 6, 7, 8th coefficients are needed. And both two segments forthe 1˜3th coefficients and two segments for the 6˜9th coefficients arerequired for being compressed in current segment for 4˜5th coefficients.

3) Level 3 Inverse Transform

Only low-pass components in level 2 transform are related in level 3inverse transform, and thus the following coefficients for derivinglow-pass coefficients are enough to be considered.

C _(2s−2,j) ^(2c) , C _(2s−1,j) ^(2c) , C _(2s,j) ^(2c) , C _(2s+1,j)^(2c) , C _(2s+2,j) ^(2c) , C _(2s+3,j) ^(2c) , C _(2s+4,j) ^(2c)

The results of level 3 inverse transform corresponding to the abovecoefficients are as follows.

x _(2s−2,j) ^(3c) , x _(2s−1,j) ^(3c) , x _(2s,j) ^(3c) , x _(2s+1,j)^(3c) , x _(2s+2,j) ^(3c) , x _(2s+3,j) ^(3c) , x _(2s+4,j) ^(3c)

Coefficients required for level 3 column direction real number andinteger number inverse transform are summarized as shown in [Table 6]and [Table 7].

TABLE 6 Transform Result low-pass coefficients high-pass coefficientsx_(2s−2,j) ^(3c) C_(s−2,j) ^(3c), C_(s−1,j) ^(3c), C_(s,j) ^(3c)D_(s−3,j) ^(3c), D_(s−2,j) ^(3c), D_(s−1,j) ^(3c), D_(s,j) ^(3c)x_(2s−1,j) ^(3c) C_(s−2,j) ^(3c), C_(s−1,j) ^(3c), C_(s,j) ^(3c),C_(s+1,j) ^(3c) D_(s−3,j) ^(3c), D_(s−2,j) ^(3c), D_(s−1,j) ^(3c),D_(s,j) ^(3c), D_(s+1,j) ^(3c) x_(2s,j) ^(3c) C_(s−1,j) ^(3c), C_(s,j)^(3c), C_(s+1,j) ^(3c) D_(s−2,j) ^(3c), D_(s−1,j) ^(3c), D_(s,j) ^(3c),D_(s+1,j) ^(3c) x_(2s+1,j) ^(3c) C_(s−1,j) ^(3c), C_(s,j) ^(3c),C_(s+1,j) ^(3c), C_(s+2,j) ^(3c) D_(s−2,j) ^(3c), D_(s−1,j) ^(3c),D_(s,j) ^(3c), D_(s+1,j) ^(3c), D_(s+2,j) ^(3c) x_(2s+2,j) ^(3c) C_(s,j)^(3c), C_(s+1,j) ^(3c), C_(s+2,j) ^(3c) D_(s−1,j) ^(3c), D_(s,j) ^(3c),D_(s+1,j) ^(3c), D_(s+2,j) ^(3c) x_(2s+3,j) ^(3c) C_(s,j) ^(3c),C_(s+1,j) ^(3c), C_(s+2,j) ^(3c), C_(s+3,j) ^(3c) D_(s−1,j) ^(3c),D_(s,j) ^(3c), D_(s+1,j) ^(3c), D_(s+2,j) ^(3c), D_(s+3,j) ^(3c)x_(2s+4,j) ^(3c) C_(s+1,j) ^(3c), C_(s+2,j) ^(3c), C_(s+3,j) ^(3c)D_(s,j) ^(3c), D_(s+1,j) ^(3c), D_(s+2,j) ^(3c), D_(s+3,j) ^(3c)

TABLE 7 Transform Result low-pass coefficients high-pass coefficientsx_(2s−2,j) ^(3c) C_(s−1,j) ^(3c) D_(s−2,j) ^(3c), D_(s−1,j) ^(3c)x_(2s−1,j) ^(3c) C_(s−2,j) ^(3c), C_(s−1,j) ^(3c), C_(s,j) ^(3c),C_(s+1,j) ^(3c) D_(s−3,j) ^(3c), D_(s−2,j) ^(3c), D_(s−1,j) ^(3c),D_(s,j) ^(3c), D_(s+1,j) ^(3c) x_(2s,j) ^(3c) C_(s,j) ^(3c) D_(s−1,j)^(3c), D_(s,j) ^(3c) x_(2s+1,j) ^(3c) C_(s−1,j) ^(3c), C_(s,j) ^(3c),C_(s+1,j) ^(3c), C_(s+2,j) ^(3c) D_(s−2,j) ^(3c), D_(s−1,j) ^(3c),D_(s,j) ^(3c), D_(s+1,j) ^(3c), D_(s+2,j) ^(3c) x_(2s+2,j) ^(3c)C_(s+1,j) ^(3c) D_(s,j) ^(3c), D_(s+1,j) ^(3c) x_(2s+3,j) ^(3c) C_(s,j)^(3c), C_(s+1,j) ^(3c), C_(s+2,j) ^(3c), C_(s+3,j) ^(3c) D_(s−1,j)^(3c), D_(s,j) ^(3c), D_(s+1,j) ^(3c), D_(s+2,j) ^(3c), D_(s+3,j) ^(3c)x_(2s+4,j) ^(3c) C_(s+2,j) ^(3c) D_(s+1,j) ^(3c), D_(s+2,j) ^(3c)

As shown in [Table 6] and [Table 7], maximum previous 3 and next 3coefficients are required for sth coefficients as a center. Since thenumber of coefficients corresponding to one segment in level 3 inversetransform is 1, minimum number of segment required for beingdecompressed in this stage is previous 3 and next 3 segments.

Based on the results of the above analysis, in order to decompress aparticular area, there need physical storage position and lengthinformation of the segment corresponding to the area ranged frombeginning segment number −3 to ending segment number +3, not just thesegment corresponding to the area ranged from the beginning segmentnumber to the ending segment number, The index information of thepresent invention is generated to identify the physical positioninformation of each segment to be actually read in consideration of theabove characteristics.

In conclusions, the storage position of any BPE coded segment in thepresent invention can be stored to be indexed by using the informationincluded in source packet, and the satellite image can be quicklyserviced to customers by decompressing the corresponding segment asindependently as possible by referring to the storage position foraccessing said segment stored in said indexed position. In other words,conventionally compressed satellite image is received and decompressed,and then satellite image product is produced for the final customersafter performing some parts of correction processes. However, in thepresent invention, the compressed satellite image is stored in the stateof being un-decompressed and the image area required for production canbe separately processed by accessing randomly to the compressed imagesection corresponding to the area requested by customers. Moreover,conventionally the entire transformed image frame should be decoded andinverse transformed in order to decode and inverse transform any BPEencoded segment due to the characteristics of wavelet transformperforming wavelet transform the image frame-by-frame. However, in thepresent invention, said any BPE encoded segment can be decompressed byreferring to only previous and next 3 segments more than the beginningnumber of said any BPR encoded segment.

While the invention has been disclosed with respect to a limited numberof embodiments and explained by referring to embodiments illustrated inaccompanying drawings, a person skilled in the art, having the benefitof this disclosure, will comprehend numerous and equivalentmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thetrue spirit and scope of the present invention.

In the claims:
 1. An apparatus for partitioned processing of compressedsatellite image, which is characterized by comprising: a receiving unitwhich receives source packet including high resolution wavelettransformed compressed satellite image segments continuously transmittedfrom satellites; an extracting unit which extracts compressed satelliteimage segments and the length information of said compressed satelliteimage segments from said received source packet; an index generatingunit which generates index of said compressed satellite image segmentsby using said length information and consecutive beginning position; anda storage unit which stores said compressed satellite image segments andsaid generated index information.
 2. The apparatus according to claim 1,wherein said source packet comprises: variable sized compressedsatellite image segments; source data comprising auxiliary data fieldcomprising attitude and position information of satellite; packet datacomprising header data comprising property information of said sourcedata; the length information of said packet data.
 3. The apparatusaccording to claim 1, wherein one of said compressed satellite imagesegments is generated through wavelet transform and BPE (bit planeencoding).
 4. The apparatus according to claim 1, wherein saidconsecutive beginning position is calculated by extracting theconsecutive beginning position of next compressed satellite imagesegments by adding the beginning position of previous compressedsatellite image segments to the length of previous compressed satelliteimage segments.
 5. The apparatus according to claim 1, said storedcompressed satellite image segments are independently decompressed byadditionally referring to previous 3 segments and next 3 segments.
 6. Amethod for partitioned processing of compressed satellite image, whichis characterized by comprising the steps of: receiving source packetincluding high resolution wavelet transformed compressed satellite imagesegments continuously transmitted from satellite; extracting compressedsatellite image segments and the length information of said compressedsatellite image segments from said received source packet; generatingindex of said compressed satellite image segments by using said lengthinformation and consecutive beginning position; and storing saidcompressed satellite image segments and said generated indexinformation.
 7. The method according to claim 6, wherein said sourcepacket comprises variable sized compressed satellite image segments;source data comprising auxiliary data field comprising attitude andposition information of satellite; packet data comprising header datacomprising property information of said source data; and the lengthinformation of said packet data.
 8. The method according to claim 6,wherein said compressed satellite image segments are generated throughwavelet transform and BPE (bit plane encoding).
 9. The method accordingto claim 6, wherein said consecutive beginning position is calculated byextracting the consecutive beginning position of next compressedsatellite image segments by adding the beginning position of previouscompressed satellite image segments to the length of previous compressedsatellite image segments.
 10. The method according to claim 1, saidstored compressed satellite image segments are independentlydecompressed by additionally referring to previous 3 segments and next 3segments.